Do the Fama–French Factors Proxy for Innovations in Predictive Variables?

The Fama–French factors HML and SMB are correlated with innovations in variables that describe investment opportunities. A model that includes shocks to the aggregate dividend yield and term spread, default spread, and one‐month Treasury‐bill yield explains the cross section of average returns better than the Fama–French model. When loadings on the innovations in the predictive variables are present in the model, loadings on HML and SMB lose their explanatory power for the cross section of returns. The results are consistent with an ICAPM explanation for the empirical success of the Fama–French portfolios.     

Predicting credit card fraud with Sarbanes‐Oxley assessments and Fama‐French risk factors

This research developed and tested machine learning models to predict significant credit card fraud in corporate systems using Sarbanes‐Oxley (SOX) reports, news reports of breaches and Fama‐French risk factors (FF). Exploratory analysis found that SOX information predicted several types of security breaches, with the strongest performance in predicting credit card fraud. A systematic tuning of hyperparamters for a suite of machine learning models, starting with a random forest, an extremely‐randomized forest, a random grid of gradient boosting machines (GBMs), a random grid of deep neural nets, a fixed grid of general linear models where assembled into two trained stacked ensemble models optimized for F1 performance; an ensemble that contained all the models, and an ensemble containing just the best performing model from each algorithm class. Tuned GBMs performed best under all conditions. Without FF, models yielded an AUC of 99.3% and closeness of the training and validation matrices confirm that the model is robust. The most important predictors were firm specific, as would be expected, since control weaknesses vary at the firm level. Audit firm fees were the most important non‐firm‐specific predictors. Adding FF to the model rendered perfect prediction (100%) in the trained confusion matrix and AUC of 99.8%. The most important predictors of credit card fraud were the FF coefficient for the High book‐to‐market ratio Minus Low factor. The second most influential variable was the year of reporting, and third most important was the Fama‐French 3‐factor model R² – together these described most of the variance in credit card fraud occurrence. In all cases the four major SOX specific opinions rendered by auditors and the signed SOX report had little predictive influence.     

Constructing and Testing Alternative Versions of the Fama–French and Carhart Models in the UK

This paper constructs and tests alternative versions of the Fama–French and Carhart models for the UK market with the purpose of providing guidance for researchers interested in asset pricing and event studies. We conduct a comprehensive analysis of such models, forming risk factors using approaches advanced in the recent literature including value‐weighted factor components and various decompositions of the risk factors. We also test whether such factor models can at least explain the returns of large firms. We find that versions of the four‐factor model using decomposed and value‐weighted factor components are able to explain the cross‐section of returns in large firms or in portfolios without extreme momentum exposures. However, we do not find that risk factors are consistently and reliably priced.     

A note on the relationship between Fama–French risk factors and innovations of ICAPM state variables

This note examines, over various time scales, the extent to which SMB (the difference between the average returns of the small-stock portfolios and big-stock portfolios) and HML (the difference in returns between the high-BM portfolios and low-BM portfolios) factors share information with the innovations of state variables, which are interpreted as alternative investment opportunities. To examine the relationship, we adopt a new and innovative approach of wavelet analysis as our main empirical method. It is found that SMB and HML may play only a limited role in capturing alternative investment opportunities in the short run, but they share much information with alternative investment opportunities in the long run.     

Uncertainty,Time‐Varying Fear,and Asset Prices

I construct an equilibrium model that captures salient properties of index option prices, equity returns, variance, and the risk‐free rate. A representative investor makes consumption and portfolio choice decisions that are robust to his uncertainty about the true economic model. He pays a large premium for index options because they hedge important model misspecification concerns, particularly concerning jump shocks to cash flow growth and volatility. A calibration shows that empirically consistent fundamentals and reasonable model uncertainty explain option prices and the variance premium. Time variation in uncertainty generates variance premium fluctuations, helping explain their power to predict stock returns.     

Creating Fama and French Factors with Style

This paper utilizes Frank Russell style portfolios to create useful proxies for the Fama and French (1992) factors. The proxy‐mimicking portfolios are shown to represent a pervasive source of exposure across U.S. industry portfolios and to generally possess similar properties to those utilized in the finance literature. Further, a set of multivariate asset‐pricing tests of the three‐factor Fama and French asset‐pricing (FF) model based on the proxy factors fails to reject the model. However, these tests do not reveal strong evidence of significantly positive risk premiums, particularly in the case of the size and book‐to‐market factors.     

Use of the Proceeds and Long‐term Performance of French SEO Firms

This paper examines the long‐term stock performance of French SEO with rights by looking at the intended use of the proceeds. Firms that raise equity for pure capital structure motives are separated from the ones that use the SEO proceeds to finance specific investment projects. Issuers in the first category are concerned about preserving their financial flexibility and they are expected to evolve in a capital structure irrelevancy framework. On the other hand, issuers in the second category are more inclined to be sensitive to adverse selection problems or agency conflicts and thus, they should be more exposed to under‐reaction on the long‐run. According to a matching firm methodology, ‘Financing New Investment’ issuers underperform their benchmark at a rate of 4% to 8% per year over a 36‐month horizon while ‘Capital Structure’ issuers do not show any abnormal performance. These results are robust according to alternative Beta pricing models. In addition, managers of both issuer's types time the SEO after a period of positive abnormal performance in order to sell overpriced securities. However, only the ‘Financing New Investment’ sample experiences a performance reversal; the abnormal returns decreasing gradually from the issue on, to become significantly negative 24 months after the event.